1 Executive Summary

  • The aim of this report is to find out what kind of correlations are between weather and alcohol sales in Sydney.
  • The main discoveries are …



2 Full Report

2.1 Initial Data Analysis (IDA)

RStudio has many data sets already loaded in. The example below uses preloaded data direct from RStudio example dataset: mtcars.

Read about the mtcars data set.

In the rmd file, you will see how you can load your own dataset from either 1) an online source using a URL or 2) a local file on your own computer.

# LOAD DATA
library(ggplot2)
data = read.csv("~/Desktop/usyd-2018-b/data1001/project1/data/data/ProcessedData.csv")

# Quick look at top 5 rows of data
head(data)
##    X       Date Receipt.Number Quantity Subtotal Sales.Tax Total Paid
## 1  0 2018-08-06          18179        1    24.55      2.45 27.00   NA
## 2  4 2018-08-06          18178        2    21.82      2.18 24.00   NA
## 3  7 2018-08-06          18177        3    42.73      4.27 47.00   NA
## 4 11 2018-08-06          18176        1    24.55      2.45 27.00   NA
## 5 16 2018-08-06          18175        5    28.23      2.82 31.05   NA
## 6 21 2018-08-06          18174        1    30.90      3.09 33.99   NA
##                                                                                    Details
## 1                                                                         1 X Cirillo Rose
## 2                                                     2 X Fever Tree Elderflower Tonic 4pk
## 3        2 X Ps40 Smoked Lemonade + 1 X Athletes of Wine Vino Athletico Macedon Pinot noir
## 4 1 X Empty Wine Bottle 750ml + 1 X Unico Zelo Harvest Sauvignon Blanc KEG + -1 X Discount
## 5          3 X Frenchies Kolsch 330ml + 3 X Frenchies Comet Pale Ale 330ml + -1 X Discount
## 6                                                1 X Domaine Thomson - Explorer Pinot Noir
##       Time Maximum.temperature..Degree.C. Rainfall.amount..millimetres.
## 1 18:42:40                             19                             0
## 2 18:03:54                             19                             0
## 3 17:45:58                             19                             0
## 4 17:32:56                             19                             0
## 5 16:26:31                             19                             0
## 6 15:05:09                             19                             0
## Size of data
# For the mtcars dataset, there are 32 rows (the types of cars) and 11 variables (properties of the cars).
dim(data)
## [1] 11870    12
## R's classification of data
class(data)
## [1] "data.frame"
## R's classification of variables
str(data)
## 'data.frame':    11870 obs. of  12 variables:
##  $ X                             : int  0 4 7 11 16 21 24 34 37 40 ...
##  $ Date                          : Factor w/ 404 levels "2017-06-24","2017-06-25",..: 404 404 404 404 404 404 404 404 404 404 ...
##  $ Receipt.Number                : Factor w/ 11870 levels "10000","10001",..: 8108 8107 8106 8105 8104 8103 8102 8101 8100 8079 ...
##  $ Quantity                      : num  1 2 3 1 5 1 5 1 1 3 ...
##  $ Subtotal                      : num  24.6 21.8 42.7 24.6 28.2 ...
##  $ Sales.Tax                     : num  2.45 2.18 4.27 2.45 2.82 3.09 2.82 3.27 1.82 2.49 ...
##  $ Total                         : num  27 24 47 27 31.1 ...
##  $ Paid                          : logi  NA NA NA NA NA NA ...
##  $ Details                       : Factor w/ 6558 levels "-1 X Adelaide Hills Distillery Dry Vermouth",..: 1075 5396 5594 1609 5787 1435 2251 1095 4873 3352 ...
##  $ Time                          : Factor w/ 9974 levels "00:28:37","01:26:38",..: 8960 8071 7704 7424 6144 4551 3576 3415 3006 2451 ...
##  $ Maximum.temperature..Degree.C.: num  19 19 19 19 19 19 19 19 19 19 ...
##  $ Rainfall.amount..millimetres. : num  0 0 0 0 0 0 0 0 0 0 ...
#sapply(mtcars, class)

Summary:

  • The data came from two different sources. The weather data came in two different data sets from the [Bureau of Meteorology] (http://www.bom.gov.au/climate/data/) website and the sale data of alcoholic beverages came from a local store in Sydney. We are using data from both resources and combining the into one big dataset. a
  • The data is/is not valid because The data is valid because we are getting
  • Possible issues include …
  • Each row represents …
  • Each column represents …


2.2 Research Question 1

How does the maximum temperature affect the consumer decision when purcahsing alcohol?

Looking at the values of the median and mean purchase transactions we see that there is not much change in consumer behaviour over the temperature ranges 15-35 degrees. However the more extreme temperature values have more of an effect. During the coldest times (10-15 degrees) there is a definate drop in amount of money spent per purchase. During the very hottest periods 40-45 degrees there is also a massive drop in amount spent on each transaction. However it is worth noting that there were very few transactions during that time. Another very interesting spike in sales occured at the 35-40 temperature range. This could be because people drink more alcohol, however these would be regular temperatures during Christamas time when people are on vacation and drink more alcohol in general.

temp = data$Maximum.temperature..Degree.C.
data$tempGroups = cut(temp, c(10,15,20,25,30,35,40,45))

#Transaction sizes for each temperature range
meanPerPerson = aggregate(data$Total ~ data$tempGroups, data, mean)
medPerPerson = aggregate(data$Total ~ data$tempGroups, data, median)
transactions = merge(x = meanPerPerson, y = medPerPerson, by='data$tempGroups')
names(transactions) = c('Temperature', 'Mean_total', 'Median_total')
transactions
##   Temperature Mean_total Median_total
## 1     (10,15]   43.73074         30.0
## 2     (15,20]   54.03240         34.0
## 3     (20,25]   55.60269         34.5
## 4     (25,30]   54.05537         34.5
## 5     (30,35]   50.16873         33.0
## 6     (35,40]   62.94938         38.4
## 7     (40,45]   32.85000         27.0
 ggplot(transactions, aes(Temperature, Mean_total)) + geom_bar(stat="identity", position = "dodge") 

 ggplot(transactions, aes(Temperature, Median_total)) + geom_bar(stat="identity", position = "dodge")

 #Number of transactions per temperature range
 barplot(table(data$tempGroups))

 #Total money spent for each temoerature range
 totalPerDay = aggregate(data$Total ~ data$tempGroups, data, sum)
 nrOfDaysPerTemp = aggregate(data$Date ~ data$tempGroups, data, function(x) length(unique(x)))
 totals = merge(x = totalPerDay, y = nrOfDaysPerTemp, by='data$tempGroups')
 names(totals) = c('Temperature', 'Total', 'NrOfDays')
 totals['meanPerDay'] = round(totals$Total / totals$NrOfDays, 1)
 
 ggplot(totals, aes(Temperature, meanPerDay)) + geom_bar(stat="identity", position = "dodge") 

Summary:

2.3 Research Question 2

How does rainfall affect the consumer decision when purchasing alcohol?

Insert text and analysis.

Summary:

2.4 Research Question 3

How does the time of year affect the consumer decision when purchasing alcohol?

Other possible research questions: What time of day do people buy their alcohol?

Insert text and analysis.

Summary:


3 Conclusions

Insert text.


4 References

Style: APA


5 Personal reflection on group work

  • The way I contributed was
  • What I learnt about group work was …


6 Beyond the Basics - extending your abilities with RMarkdown

This quick reference guide will cover some basic RMarkdown for use in your projects.


6.1 Lists

Here is a basic list:

  • To do 1

  • To do 2

  • To do 3


6.3 Tables

Here is a simple table.

Tables Are Cool
col 3 is right-aligned $1600
col 2 is centered $12
zebra stripes are neat $1


6.4 Images

Here is am image. It has not been adjusted in the rmd file, so represents the true size of the original image. This image is sourced directly from an online url.

To learn more about adding images directly from your own computer, see the comments in this rmd file.


Image source: https://petcube.com/blog/10-all-important-kitten-supplies-infographic/


6.5 Videos

Below you will find a video embedded into your RMarkdown file. Change the YouTube link in the rmd file to get a different video.


6.6 LaTeX

You can even use LaTeX in an RMarkdown document!

For example, how could you work out \(\sum_{i=1}^{5} x_{i}^3\)?


6.7 R Code

Here is an R code chunk:

Try the following commands in R.

1+ exp(3) + sin(0.5)
x=c(1,2,3)
x^2
sum(x)

Here is some in-line code in-line code. You can put any R code here for display, e.g. sum(x)


7 RMarkdown Resources

Check out the resources below for more information on RMarkdown.

How to use R Markdown

Mastering Markdown